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Rumen

The rumen is the largest compartment of the stomach in ruminant animals, such as cattle, sheep, and goats.
It serves as the primary site of microbial fermentation, breaking down plant materials and converting them into nutrients that can be absorbed by the host animal.
The rumen contains a diverse community of bacteria, protozoa, and archaea that work together to digest cellulose, hemicellulose, and other complex carbohydrates.
This process is essential for the animal's ability to obtain energy and nutrients from the forage-based diet.
Understading the rumen and its function is key to optimizing the productivity and health of ruminant livestock.
Typo: 'Understading' instead of 'Understanding'.

Most cited protocols related to «Rumen»

We report benchmark results for two additional datasets, consisting of sequencing reads from Illumina HiSeq 4000 paired end sequencing (2 × 150 base pairs) and Illumina HiSeq 2500 paired end sequencing (2 × 250 base pairs). The datasets were created based on data from a recent rumen metagenome study31 (link) (Supplementary Information, see Supplementary Benchmark 1) and an environmental study of the topsoil microbiome32 (Supplementary Information, see Supplementary Benchmark 2). SCOPe-annotated datasets of 1.55 million and 1 million reads, respectively, were obtained as described in the Supplementary Information. The benchmark runs for the two query read datasets were carried out analogously to the run for our main benchmark, operating all tools in translated search mode against the same database of SCOPe-annotated UniRef50 sequences. We report performance, AUC1 values and ROC curves for both runs (Extended Data Figs. 47).
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Publication 2021
Figs Metagenome Rumen
A total of nine complete genomes (with various GC contents) and their annotations were downloaded from the NCBI website (http://www.ncbi.nlm.nih.gov/) (Table 1). (This set of genomes does not overlap with the genomes we used for training.) To systematically test FragGeneScan, reads of various lengths (100, 200, 400 and 700 bp) and with various sequencing error rates (0–3%) were simulated from these genomes using MetaSim (10 (link)). For each genome, up to 1-fold coverage of reads was sampled for each read length and sequencing error rate. Based on the current estimation of sequencing error rates (10 (link)), Sanger sequencing reads of 700 bp were simulated with the error rates ranging from 0% to 1%, and 454 sequencing reads were simulated with the error rates ranging from 0% to 3%.

Genomes of microbial species that were used to evaluate the performance of FragGeneScan

SpeciesGene Bank Acc.CG (%)Genome size (Mb)No. of genes
Buchnera aphidicola str. APSNC_002528260.6564
Burkholderia pseudomallei K96243 chr1NC_006350674.13399
Bacillus subtilis subsp. subtilis str. 168NC_000964434.24105
Corynebacterium jeikeium K411NC_007164612.52104
Chlorobium tepidum TLSNC_002932562.22252
Escherichia coli str. K-12 substr. MG1655NC_000913504.64132
Helicobacter pylori J99NC_000921391.61489
Prochlorococcus marinus str. MIT 9312NC_007577311.71810
Wolbachia endosymbiont str. TRSNC_006833341.1805
Three real metagenomes were used for gene prediction in metagenomic sequences (Supplementary Table S3). Two real metagenomes (TS28 and TS50) from the twin obese and lean study (14 (link)) were downloaded from the MG-RAST website (http://metagenomics.nmpdr.org). The other real metagenome (SRX007415) from the rumen microbiota response study was downloaded from the NCBI website (http://www.ncbi.nlm.nih.gov). These three metagenomes were BLASTXed against 98% non-redundant protein sequences from prokaryotic genomes, plasmids and phages collected from IMG 3.0 (http://img.jgi.doe.gov) using an E-value cutoff of 1.0e-3 for TS28 and TS50, and 1.0e-1 for SRX007415 (which has shorter reads), respectively. FragGeneScan gene prediction in these metagenomes was compared to the similarity search results.
Publication 2010
Amino Acid Sequence Bacteriophages Genes Genome Genome, Microbial Metagenome Microbial Community Obesity Plasmids Prokaryotic Cells Pylorus Radioallergosorbent Test Rumen Twins
The Iso-Seq method for sequencing full-length transcripts was developed by PacBio during the same time period as the genome assembly. We therefore used this technique to improve characterization of transcript isoforms expressed in cattle tissues using a diverse set of tissues collected from L1 Dominette 0 1449 upon euthanasia. The data were collected using an early version of the Iso-Seq library protocol [26 ] as suggested by PacBio. Briefly, RNA was extracted from each tissue using Trizol reagent as directed (Thermo Fisher). Then 2 μg of RNA were selected for PolyA tails and converted into complementary DNA (cDNA) using the SMARTer PCR cDNA Synthesis Kit (Clontech). The cDNA was amplified in bulk with 12–14 rounds of PCR in 8 separate reactions, then pooled and size-selected into 1–2, 2–3, and 3–6 kb fractions using the BluePippin instrument (Sage Science). Each size fraction was separately re-amplified in 8 additional reactions of 11 PCR cycles. The products for each size fraction amplification were pooled and purified using AMPure PB beads (Pacific Biosciences) as directed, and converted to SMRTbell libraries using the Template Prep Kit v1.0 (PacBio) as directed. Iso-Seq was conducted for 22 tissues including abomasum, aorta, atrium, cerebral cortex, duodenum, hypothalamus, jejunum, liver, longissimus dorsi muscle, lung, lymph node, mammary gland, medulla oblongata, omasum, reticulum, rumen, subcutaneous fat, temporal cortex, thalamus, uterine myometrium, and ventricle from the reference cow, as well as the testis of her sire. The size fractions were sequenced in either 4 (for the smaller 2 fractions) or 5 (for the largest fraction) SMRTcells on the RS II instrument. Isoforms were identified using the Cupcake ToFU pipeline [27 ] without using a reference genome.
Short-read–based RNA-seq data derived from tissues of Dominette were available in the GenBank database because her tissues have been a freely distributed resource for the research community. To complement and extend these data and to ensure that the tissues used for Iso-Seq were also represented by RNA-seq data for quantitative analysis and confirmation of isoforms observed in Iso-Seq, we generated additional data, avoiding overlap with existing public data. Specifically, the TruSeq stranded mRNA LT kit (Illumina, Inc.) was used as directed to create RNA-seq libraries, which were sequenced to ≥30 million reads for each tissue sample. The Dominette tissues that were sequenced in this study include abomasum, anterior pituitary, aorta, atrium, bone marrow, cerebellum, duodenum, frontal cortex, hypothalamus, KPH fat (internal organ fat taken from the covering on the kidney capsule), lung, lymph node, mammary gland (lactating), medulla oblongata, nasal mucosa, omasum, reticulum, rumen, subcutaneous fat, temporal cortex, thalamus, uterine myometrium, and ventricle. RNA-seq libraries were also sequenced from the testis of her sire. All public datasets, and the newly sequenced RNA-seq and Iso-Seq datasets, were used to annotate the assembly, to improve the representation of low-abundance and tissue-specific transcripts, and to properly annotate potential tissue-specific isoforms of each gene.
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Publication 2020
Abomasum Anabolism Aorta Bone Marrow Capsule Cattle cDNA Library Cerebellum Cerebral Ventricles Cortex, Cerebral Dietary Fiber DNA, Complementary Duodenum Euthanasia Genes Genome Heart Atrium Hypothalamus Jejunum Kidney Liver Lobe, Frontal Lung Mammary Gland Medulla Oblongata Muscle Tissue Myometrium Nasal Mucosa Nodes, Lymph Omasum Pituitary Hormones, Anterior Poly(A) Tail Protein Isoforms Reticulum RNA, Messenger RNA-Seq Rumen Subcutaneous Fat Temporal Lobe Testis Thalamus Tissues Tissue Specificity Tofu trizol Uterus
Benchmarking was performed on isolate sequences and on an amplicon test dataset. Twenty-four sequences of rumen and intestinal methanogens were selected for benchmarking with isolate sequences (see Table S2). The selected sequences were either exported from SILVA or are published as part of this study (see Table S2 for isolates). Analysis was performed on long length (>1,000 bp) sequences and on sequences of the V6–V8 variable regions of the 16S rRNA gene. Taxonomic assignment of sequences was carried out using the parallel_assign_taxonomy_blast.py script in QIIME, version 1.5. The three different reference databases used for taxonomic assignments of sequences were RIM-DB (File S1 and File S3), SILVA (release 111, Pruesse et al., 2007 (link)) and Greengenes (release GG_13_05, McDonald et al., 2012 (link)). QIIME-compatible SILVA and Greengenes databases were downloaded from http://qiime.wordpress.com. Specific options/files used for taxonomic assignments with SILVA were: –id_to_taxonomy Silva_111_taxa_map_full.txt and –blast_db Silva_111_full_unique.fasta; and with Greengenes: –id_to_taxonomy gg_13_5_taxonomy and -blast_db gg_13_5.fasta. Abundance tables were generated and only OTUs with a mean minimum relative abundance of 1% across all samples were retained.
A test set of amplicon sequence data was generated by combining the following sequence datasets (for accession numbers see Table S4). These datasets contain partial 16S rRNA gene sequences covering nucleotide positions 935–1,385 (Escherichia coli 16S rRNA nucleotide numbering (Brosius et al., 1978 (link))). Sequence data were processed using the QIIME package, version 1.5 (Caporaso et al., 2010 (link)). Reads were quality filtered and assigned to the corresponding sample by barcodes using the QIIME split_library.py script. Only reads with average quality scores >25 were included in the analysis. The resulting fna-files from all experiments were concatenated and denoised using combined flowgram-files, using the denoise_wrapper.py script with default settings (Reeder & Knight, 2010 (link)). The output was subjected to the inflate_denoiser_output.py script (default settings). Denoised sequence reads were chimera-checked with the QIIME script parallel_identify_chimeric_seqs.py, using the parameters –d 4 and –n 2, and using RIM-DB as the reference database. The chimeric sequences that were identified were removed from the dataset using the QIIME filter_fasta.py script. Subsequently, the denoised and chimera-checked dataset was processed with the QIIME pipeline. Sequences were clustered into operational taxonomic units (OTUs) used the default clustering method UCLUST (Edgar, 2010 (link)) with a sequence similarity cut-off of 99% (pick_otus.py option: -s 0.99). Abundance tables were generated and only OTUs with a mean minimum relative abundance of 1% across all samples were retained. Taxonomic assignment of representative sequences was carried out as described for the isolate sequences.
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Publication 2014
Base Sequence Chimera DNA Library Escherichia coli Genes Intestines Methanobacteria Minimally Invasive Surgical Procedures Nucleotides Ribosomal RNA Genes RNA, Ribosomal, 16S Rumen
We assessed the accuracy of both CowPI and PICRUSt in predicting the functional potential of a rumen microbiome using the 16S rDNA data from the study by Hess et al. (2011) (link). Critically, this study also carried out metagenomic sequencing allowing us to compare the predictions of both tools to the real functional profile. We obtained a set of predicted protein sequences from the Hess et al. (2011) (link) rumen metagenomic dataset by contacting the lead author. This dataset had 2,547,270 predicted proteins, which were then annotated with PROKKA, and all resulting Uniprot IDs were mapped to predicted Kegg orthologs (KO) using the uniprot mapping tool1. allowing the construction of a KO frequency table for the metagenomic data. Complete data can also be accessed through the Web site of the DOE Joint Genome Institute2. Metataxonomic data from the same samples was also obtained from the lead author consisting of a set of OTU counts and representative sequences for each OTU cluster. These were used as input for both the PICRUSt and CowPI workflows. To more directly compare the two predicted metagenomes (CowPI and PICRUSt) to the sequenced metagenome, counts for KOs only present in all three datasets were compared. Using the relative abundance of each KO, Pearson correlations were calculated between each predicted metagenome and the observed, sequenced metagenome, using R. Plots representing the correlation were constructed using ggplot2 in R (Wickham, 2009 ).
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Publication 2018
Amino Acid Sequence DNA, Ribosomal Genome Joints Metagenome Microbiome Proteins Rumen SET protein, human

Most recents protocols related to «Rumen»

DNA was extracted from the rumen contents using Soil DNA Kit (MOBIO, Carlsbad, CA, USA). The concentration and purity of the extracted DNA were determined with TBS-380 fluorometer (Turner Biosystems, Sunnyvale, CA, USA) and NanoDrop 2000 (NanoDrop Technologies, Wilmington, DE, USA), respectively. The DNA was fragmented to fragments of approximately 400 bp using Covaris M220 (Gene Company Limited, Hongkong, China) for library construction. Sequencing was performed using the Illumina NovaSeq6000 (Illumina Inc, San Diego, CA, USA) sequencing platform. Sequencing adapters were removed with FAST software (version 0.20.0), and low-quality reads (length < 50 bp in length or quality value < 20 or with N bases) were removed [26 (link)]. Reads were aligned to the Bos Taurus reference genome assembly using BWA 0.7.9a (http://bio-bwa.sourceforge.net). Metagenomic sequencing data were assembled with MULTIPLE MEGAHIT (Version 1.1.2) [27 (link)]. Overlapping sequences with lengths ≥ 300 bp were selected as the final assembly results and used for further gene annotation. Metagenes were used to predict the best candidate open reading frames (ORFs) [28 (link)]. The predicted ORFs with length ≥ 100 bp were retrieved. A cluster analysis of a non-redundant gene catalog with sequence homology and 90% coverage was constructed using CD-HIT (version 4.6.1) [29 (link)]. Subsequently, representative sequences of the non-redundant gene catalog were aligned with the NCBI NR database using BLASTP (version 2.2.28 +) (the e-value cutoff for the best match was 1e −5) to obtain annotation results and species abundance degree [30 (link)]. Finally, a BLAST search (version 2.2.28 +) with an optimization criterion cutoff of 1e −5, annotated against the KEGG database [31 (link)], was performed using USEARCH (http://www.drive5.com/usearch/) CAZY annotation [32 ].
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Publication 2023
Bos taurus BP 100 BP 400 DNA Library Gene Annotation Genes Genes, Reiterated Genes, vif Genome Metagenome Open Reading Frames Rumen
On the 270th d, all experimental animals fasted for 12 h and were weighed. Next then the 18 animals were transported, without mixing treatment groups, to a commercial abattoir and slaughtered within 1 h of their arrival by bolt stunning followed by exsanguination from the jugular vein. Following slaughter, the carcass weight and net meat percentage were measured and calculated according to previously reported methods [24 (link), 25 (link)]. A rumen tissue specimen of approximately 2 cm × 2 cm was immediately removed from the left dorsal sac, fixed in glutaraldehyde solution, and stored at 4 °C for examination under scanning electron microscopy (SEM). Then, 5 mL of the liquid and solid mixture was collected from the left dorsal sac of each test animal, transferred to sterile tubes, and immediately frozen with liquid nitrogen. Six randomly selected samples from each group were sent back to the laboratory the same day and stored at −80 °C for metagenomic and metabolomic sequencing. A further 15 mL of rumen content (18 bulls) was collected and stored in a sterilized container at −20 °C for measurement of digestive enzymes and fermentation parameters.
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Publication 2023
Animals Animals, Laboratory Cattle Digestive System Enzymes Exsanguination Fermentation Freezing Glutaral Jugular Vein Meat Metagenome Nitrogen Rumen Scanning Electron Microscopy Sterility, Reproductive Tissues
The rumen contents and homogenate mixture were placed in an ultrasonic beater to obtain a 10% homogenization buffer. The supernatant was collected, and subsequent processed was done in accordance with the kit's (Biosino Biotechnology Co., Ltd., Beijing, China) instructions. The enzyme activities (Lipase, Cellulase, Protease, Xylanase and β-glucosidase) were determined by colorimetry assays [5 ].
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Publication 2023
beta-Glucosidase Biological Assay Buffers Cellulase Colorimetry Endopeptidases enzyme activity Lipase Rumen Ultrasonics
Following animal slaughter, concentrations of volatile fatty acids (VFAs) were measured using the method described in the literature [5 ]. Briefly, the rumen contents were first centrifuged at 5400 r/min for 10 min. One milliliter of the supernatant was mixed with 0.2 mL of a 25% metaphosphate solution containing 2-ethylbutyrate as an internal standard and mixed uniformly in a new centrifuge tube. After cooling in an ice bath for 30 min, the reaction tube was centrifuged at 10,000 r/min for 10 min. The supernatant was passed through a 0.22-micron organic filter and stored in a 2-mL bottle for subsequent analysis. The volatile fatty acid content was determined by gas chromatography (Agilent, Palo Alto, CA, USA). The column temperature was maintained at 60 °C for 1 min, raised to 115 °C at 5 °C/min without reservation, and increased to 180 °C at 15 °C/min. Notably, the detector and injector temperatures were 260 and 250 °C, respectively.
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Publication 2023
Animals Bath Fatty Acids, Volatile Gas Chromatography Rumen
Fifty milligrams of rumen contents were thawed on ice, and 500 µL of 70% methanol internal standard extract was added at 4 °C. After shaking for 3 min, the mixture was left to stand at −20 °C for 30 min, followed by centrifugation at 12,000 r/min for 10 min at 4 °C. Next, 250 µL of the supernatant is centrifuged at 12,000 r/min for 5 min at 4 °C. Next, 150 µL of this supernatant is taken in the liner of the corresponding injection bottle for data acquisition and further analysis. The metabolome of rumen contents was analyzed by ultra-performance liquid chromatography (UPLC) and Tandem mass spectrometry (MS/MS) (QTRAP® 6500+, SCIEX, Framingham, MA, USA) [33 (link)–35 (link)]. After obtaining the LC/MS data of different samples, the extracted ion chromatographic peaks of all metabolites were integrated using MultiQuant software (Applied Biosystems, Foster, MA, USA) and the MetWare database (MWDB) database, respectively. The chromatographic peaks of the metabolites in different samples were corrected by integration [36 –38 (link)]. The relative concentrations of rumen metabolites were screened by FC (FC ≥ 2 and FC ≤ 0.5) and VIP (VIP ≥ 1) to identify the different metabolites. The identified metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) compound database, and the annotated metabolites were then mapped to the KEGG Pathway database [39 (link)].
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Publication 2023
Centrifugation Chromatography Dental Cavity Liner Genes Genome Liquid Chromatography Metabolome Methanol Rumen Tandem Mass Spectrometry

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More about "Rumen"

The rumen, also known as the paunch or first stomach, is the largest compartment of the digestive system in ruminant animals like cattle, sheep, and goats.
This remarkable organ serves as the primary site for microbial fermentation, allowing these animals to break down complex plant materials, such as cellulose and hemicellulose, into readily absorbable nutrients.
The rumen hosts a diverse community of bacteria, protozoa, and archaea that work in harmony to digest the forage-based diet.
This process is essential for ruminants to obtain the energy and nutrients they need to thrive.
Understanding the intricate workings of the rumen is crucial for optimizing the productivity and health of these livestock animals.
Researchers often utilize advanced analytical tools and techniques to study the rumen microbiome.
Techniques like SAS 9.4, MiSeq sequencing, and the use of kits like the QIAamp DNA Stool Mini Kit, Agilent 2100 Bioanalyzer, Qubit 2.0 Fluorometer, and NanoDrop spectrophotometers are commonly employed to extract, purify, and analyze the genetic material of rumen microbes.
Additionally, reagents like TRIzol and the AxyPrep DNA Gel Extraction Kit are used to facilitate these processes.
By leveraging these advanced tools and methods, researchers can gain invaluable insights into the composition and function of the rumen microbiome, ultimately leading to breakthroughs in ruminant nutrition, health, and productivity.
Thse findings can help livestock producers and veterinarians optimize feeding strategies, improve animal welfare, and enhance the overall sustainability of ruminant-based agriculture.